Real-time Soundprism

[EN] This paper presents a parallel real-time sound source separation system for decomposing an audio signal captured with a single microphone in so many audio signals as the number of instruments that are really playing. This approach is usually known as Soundprism. The application scenario of the system is for a concert hall in which users, instead of listening to the mixed audio, want to receive the audio of just an instrument, focusing on a particular performance. The challenge is even greater since we are interested in a real-time system on handheld devices, i.e., devices characterized by both low power consumption and mobility. The results presented show that it is possible to obtain real-time results in the tested scenarios using an ARM processor aided by a GPU, when this one is present.This work has been supported by the "Ministerio de Economia y Competitividad" of Spain and FEDER under projects TEC2015-67387-C4-{1,2,3}-R.Muñoz-Montoro, AJ.; Ranilla, J.; Vera-Candeas, P.; Combarro, EF.; Alonso-Jordá, P. (2019). Real-time Soundprism. The Journal of Supercomputing. 75(3):1594-1609. https://doi.org/10.1007/s11227-018-2703-0S15941609753Alonso P, Cortina R, Rodríguez-Serrano FJ, Vera-Candeas P, Alonso-González M, Ranilla J (2017) Parallel online time warping for real-time audio-to-score alignment in multi-core systems. J Supercomput 73:126. https://doi.org/10.1007/s11227-016-1647-5Carabias-Orti JJ, Cobos M, Vera-Candeas P, Rodríguez-Serrano FJ (2013) Nonnegative signal factorization with learnt instrument models for sound source separation in close-microphone recordings. EURASIP J Adv Signal Process 2013:184. https://doi.org/10.1186/1687-6180-2013-184Carabias-Orti JJ, Rodriguez-Serrano FJ, Vera-Candeas P, Canadas-Quesada FJ, Ruiz-Reyes N (2015) An audio to score alignment framework using spectral factorization and dynamic time warping. In: 16th International Society for Music Information Retrieval Conference, pp 742–748Díaz-Gracia N, Cocaña-Fernández A, Alonso-González M, Martínez-Zaldívar FJ, Cortina R, García-Mollá VM, Alonso P, Ranilla J (2014) NNMFPACK: a versatile approach to an NNMF parallel library. In: Proceedings of the 2014 International Conference on Computational and Mathematical Methods in Science and Engineering, pp 456–465Díaz-Gracia N, Cocaña-Fernández A, Alonso-González M, Martínez-Zaldívar FJ, Cortina R, García-Mollá VM, Vidal AM (2015) Improving NNMFPACK with heterogeneous and efficient kernels for $$\beta$$ β -divergence metrics. J Supercomput 71:1846–1856. https://doi.org/10.1007/s11227-014-1363-yDriedger J, Grohganz H, Prätzlich T, Ewert S, Müller M (2013) Score-informed audio decomposition and applications. In: Proceedings of the 21st ACM International Conference on Multimedia, pp 541–544Duan Z, Pardo B (2011) Soundprism: an online system for score-informed source separation of music audio. IEEE J Sel Top Signal Process 5(6):1205–1215Duong NQ, Vincent E, Gribonval R (2010) Under-determined reverberant audio source separation using a full-rank spatial covariance model. IEEE Trans Audio Speech 18(7):1830–1840. https://doi.org/10.1109/TASL.2010.2050716Ewert S, Müller M (2011) Estimating note intensities in music recordings. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp 385–388Ewert S, Pardo B, Mueller M, Plumbley MD (2014) Score-informed source separation for musical audio recordings: an overview. IEEE Signal Process Mag 31:116–124. https://doi.org/10.1109/MSP.2013.2296076Fastl H, Zwicker E (2007) Psychoacoustics. Springer, BerlinGanseman J, Scheunders P, Mysore GJ, Abel JS (2010) Source separation by score synthesis. Int Comput Music Conf 2010:1–4Goto M, Hashiguchi H, Nishimura T, Oka R (2002) RWC music database: popular, classical and jazz music databases. In: ISMIR, vol 2, pp 287–288Goto M (2004) Development of the RWC music database. In: Proceedings of the 18th International Congress on Acoustics (ICA 2004), ppp 553–556Hennequin R, David B, Badeau R (2011) Score informed audio source separation using a parametric model of non-negative spectrogram. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp 45–48. https://doi.org/10.1109/ICASSP.2011.5946324Itoyama K, Goto M, Komatani K et al (2008) Instrument equalizer for query-by-example retrieval: improving sound source separation based on integrated harmonic and inharmonic models. In: ISMIR. https://doi.org/10.1136/bmj.324.7341.827Marxer R, Janer J, Bonada J (2012) Low-latency instrument separation in polyphonic audio using timbre models. In: International Conference on Latent Variable Analysis and Signal Separation, pp 314–321Miron M, Carabias-Orti JJ, Janer J (2015) Improving score-informed source separation for classical music through note refinement. In: ISMIR, pp 448–454Ozerov A, Févotte C (2010) Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation. IEEE Trans Audio Speech Lang Process 18:550–563. https://doi.org/10.1109/TASL.2009.2031510Ozerov A, Vincent E, Bimbot F (2012) A general flexible framework for the handling of prior information in audio source separation. IEEE Trans Audio Speech Lang Process 20:1118–1133. https://doi.org/10.1109/TASL.2011.2172425Pätynen J, Pulkki V, Lokki T (2008) Anechoic recording system for symphony orchestra. Acta Acust United Acust 94:856–865. https://doi.org/10.3813/AAA.918104Raphael C (2008) A classifier-based approach to score-guided source separation of musical audio. Comput Music J 32:51–59. https://doi.org/10.1162/comj.2008.32.1.51Rodriguez-Serrano FJ, Duan Z, Vera-Candeas P, Pardo B, Carabias-Orti JJ (2015) Online score-informed source separation with adaptive instrument models. J New Music Res 44:83–96. https://doi.org/10.1080/09298215.2014.989174Rodriguez-Serrano FJ, Carabias-Orti JJ, Vera-Candeas P, Martinez-Munoz D (2016) Tempo driven audio-to-score alignment using spectral decomposition and online dynamic time warping. ACM Trans Intell Syst Technol 8:1–20. https://doi.org/10.1145/2926717Sawada H, Araki S, Makino S (2011) Underdetermined convolutive blind source separation via frequency bin-wise clustering and permutation alignment. IEEE Trans Audio Speech Lang Process 19(3):516–527. https://doi.org/10.1109/TASL.2010.2051355Vincent E, Araki S, Theis F et al (2012) The signal separation evaluation campaign (2007–2010): achievements and remaining challenges. Signal Process 92:1928–1936. https://doi.org/10.1016/j.sigpro.2011.10.007Vincent E, Bertin N, Gribonval R, Bimbot F (2014) From blind to guided audio source separation: how models and side information can improve the separation of sound. IEEE Signal Process Mag 31:107–115. https://doi.org/10.1109/MSP.2013.229744

Nonnegative signal factorization with learnt instrument models for sound source separation in close-microphone recordings

Close-microphone techniques are extensively employed in many live music recordings, allowing for interference rejection and reducing the amount of reverberation in the resulting instrument tracks. However, despite the use of directional microphones, the recorded tracks are not completely free from source interference, a problem which is commonly known as microphone leakage. While source separation methods are potentially a solution to this problem, few approaches take into account the huge amount of prior information available in this scenario. In fact, besides the special properties of close-microphone tracks, the knowledge on the number and type of instruments making up the mixture can also be successfully exploited for improved separation performance. In this paper, a nonnegative matrix factorization (NMF) method making use of all the above information is proposed. To this end, a set of instrument models are learnt from a training database and incorporated into a multichannel extension of the NMF algorithm. Several options to initialize the algorithm are suggested, exploring their performance in multiple music tracks and comparing the results to other state-of-the-art approaches.This work was supported by the Andalusian Business, Science and Innovation Council under project P2010- TIC-6762, (FEDER) the Spanish Ministry of Economy and Competitiveness under the projects TEC2012-38142-C04-03 and TEC2012-37945-C02-0